To test predictions, we either observe natural variation and use statistical analysis to account for variation in covariates to estimate the influence of the variable we’re interested in, or we control variation in a planned manner, by manipulating one predictor variable and holding others constant. In this chapter, I review good study design and the strengths and weaknesses of observation and manipulation. I then explain that although it is easiest to describe observations and experiments separately, they lie at opposite ends of a continuum of researcher-imposed control on a study system.

We use statistical analyses to test our predictions using the measures we collect for our sample. Like all aspects of study design, we need to think carefully about our choice of analytical approach. Planning our data analysis in detail, before we collect your data, helps to determine what data we need to collect. It is very common to rush past the analysis plan and dive straight into collecting data. This is partly because statistics are not intuitive and can be intimidating. However, statistical analysis is an integral part of study design. We must understand statistics to understand the strengths, limitations, and potential biases of any research. This may seem daunting, but our understanding of statistics determines the quality of a study. The more we think about this now, the better our study will be. I begin this chapter with how to determine what sort of analyses we need and the need to consult a statistician when we design a study. Next, I cover problems associated with multiple testing and assessing multiple predictor variables. I explain how to prepare an analysis plan and suggest pre-registration.

Research integrity means conducting science in such a way that others can be confident in the methods we used and trust the findings we report. In addition to our responsibility to understand and comply with the ethical and legal obligations associated with our research, research integrity involves scrupulous honesty and the highest standards of rigour. However, a combination of our own biases, distorted career incentives, poor understanding of study design, and misuse of statistical analysis lead to practices that damage science (questionable research practices). Such practices undermine the validity of studies and increase the chance of erroneous results, leading to a literature based on false positive conclusions and studies that can’t be replicated. The inability to replicate the findings of published studies has been popularised as the replication crisis, particularly in medicine and psychology. In this chapter, I first define research misconduct and its consequences. I then review responsible practices and how to avoid questionable research practices. We’ll revisit these issues throughout the book.

It is our ethical duty to consider the possible consequences of our work and mitigate any risks, such that we avoid harm to the welfare and interests of our study animals, human participants, the environment, and the people we work with and alongside. We must also consider the effects of our research on our discipline and wider society. Reflecting on ethical dilemmas and weighing the positive and negative impacts of a project are essential to make informed decisions when planning a project and throughout a study. This can include the decision not to conduct a particular study, or to terminate it earlier than planned. In this chapter, I cover legal requirements and permits, then address the ethics of working with primates in captivity and the wild, specimen collection and working human participants. I then outline our ethical responsibilities to the natural environment, the people we work with, and the people we work alongside. I then highlight the importance of reflecting on our use of social media and the power of images, and end with our obligations to report and disseminate our findings.

A research project is not finished until we have written it up. Scientific reports have a standard format, with some variation. This should be familiar from your reading. This chapter builds on the general advice for writing in Chapter 14 and focusses on how to write a scientific report. I provide general guidance for writing your report, then cover each section of the manuscript in turn. I focus on primary research articles, because these are the main way in which we disseminate new research. Much of the advice applies more generally to theses and dissertations. Most reports have multiple authors and we must negotiate authorship fairly.

We may already be convinced of the value of studying primates, but we often need to convince others of that value in proposals, reports and papers. This chapter covers the reasons to study primates, including appreciation of their fascinating diversity and adaptations, their important ecological functions, their evolutionary relationship with humans, their socio-cultural importance, concern for their captive welfare, and their conservation status.

Statistical evidence is fundamental to science. Understanding statistics helps us to understand the literature and assess it critically, refine our research questions into testable hypotheses and predictions, design studies that are appropriate to test these predictions, evaluate whether our findings support our predictions, and derive appropriate conclusions. The dominant paradigm in primatology and allied disciplines is to test whether patterns we observe in our observations are due to more than random variation in our data. However, the statistical analyses we use to do this are very often misinterpreted. In this chapter I distinguish different kinds of variables, then introduce relationships between variables. I explain how we use statistical analysis to infer something about a theoretical population based on a sample. I introduce null hypothesis significance testing and explain common misunderstandings of this approach. I review the two types of error that arise in NHST and the concept of statistical power. I explain the need to assess and report effect sizes and confidence intervals, briefly introduce alternatives to null hypothesis statistical testing and end with how to interpret statistical results appropriately.

Like all science, studying primates is about asking the right questions in the right way. Most studies of primates fall within the life sciences, so I focus on the scientific method in this book. This chapter introduces how science works, then what it takes to be a primatologist. I outline the contents of the rest of the book and highlight the importance of keeping science healthy. I end by emphasising the need to respect other people and to promote inclusive science.

In this book, we’ve looked at how we study primates, including how we assess published studies, identify and develop a research question, formulate testable hypotheses and predictions, design and conduct a study that will test the predictions, select appropriate measures and samples, analyse the data, interpret the results, draw conclusions from the results in relation to the original question, and report the results in writing and presentations.

We find out what scientists know about a topic by searching the scientific literature. Literature searches range from a preliminary search to find out what we know about a general area to a specific search on a precise topic. As we explore a research topic, we focus our searches to identify the main open questions, the hypotheses proposed and the support for them, potential model systems and methods, and the experts in the field. Broad background reading is also fundamental preparation for a study because no study goes as planned and we may need to identify new research questions as we progress. I begin this chapter with sources of information we have available, then describe how we identify search terms and assess the quality of the literature we find, I explain the importance of reading broadly and how to choose what to read, and end with how we keep up with the literature

Scientific research is subject to serious inequalities of opportunity. Economic, political, social and cultural influences shape the opportunities available to people. Everyday and institutional practices exclude people based on aspects of their identity. These inequities intersect in complicated ways and have negative effects on both individuals and science. Some may go unnoticed, even by those who are negatively affected by them, because they are so deeply entrenched in our cultures. In this chapter, I briefly explore discrimination in relation to various aspects of identity, and how these intersect. I then describe the effects of discrimination on people and on science, and how we can help to combat inequities.

Good research design includes careful consideration of the number of independent observations (replicates) we need to test our predictions – the sample size. Some sampling decisions are beyond our control. For example, we may be limited by the number of specimens available, the animals we can observe, or the data we have at our disposal. Knowing in advance what we can and can’t test with our data will save wasted effort. This chapter covers how we use samples to study populations, the importance of statistical power, how to determine whether you have the power to test for an effect, and statistical precision.

Data collection is fun and exciting. It can also be difficult and dull at times. Things don’t always go to plan (ask other researchers about projects that didn’t work – we all have plenty of examples). In this chapter, I cover the importance of monitoring the progress of your project, being flexible and open to opportunities, being prepared for the unforeseen, collecting data rigorously and systematically, keeping data and samples safe, and being considerate of other people.

Good research needs good planning. A detailed research plan helps you determine the feasibility of your study and anticipate the issues you will face. In this chapter, I cover logistics and practicalities, how we use pilot studies to test the feasibility of a project, making a timeline, assessing risk, and budgeting.

Critical reading is an essential skill for scientists. As you read the rest of this book, you’ll come back to the literature again and again, to find out more about particular topics. Reading takes time and can be daunting, but it gets easier with experience. Reading also teaches you what goes where in a paper. The more you read, the better you will write. In this chapter I explain how to read articles, beginning with general advice, then providing questions to ask as you read each section of an article. Then I cover organising a reference collection and synthesising what you read.